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Found 5 papers, 0 papers with code
Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set.
Label-efficient audio classification through multitask learning and self-supervision
We demonstrate that, in scenarios with limited labeled training data, one can significantly improve the performance of three different supervised classification tasks individually by up to 6% through simultaneous training with these additional self-supervised tasks.
Generalization in multitask deep neural classifiers: a statistical physics approach
There has also been a recent interest in extending these analyses to understanding how multitask learning can further improve the generalization capacity of deep neural nets.
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks
Motivated by a recent study on learning robustness without input perturbations by distilling an AT model, we explore what is learned during adversarial training by analyzing the distribution of logits in AT models.
When and how epochwise double descent happens
This model is based on the hypothesis that the training data contains features that are slow to learn but informative.
